Identification and diagnosis of cervical cancer using a hybrid feature selection approach with the bayesian optimization-based optimized catboost classification algorithm
{"title":"Identification and diagnosis of cervical cancer using a hybrid feature selection approach with the bayesian optimization-based optimized catboost classification algorithm","authors":"Joy Dhar, Souvik Roy","doi":"10.1007/s12652-024-04825-8","DOIUrl":null,"url":null,"abstract":"<p>Cervical cancer is the most prevailing woman illness globally. Since cervical cancer is a very preventable illness, early diagnosis exhibits the most adaptive plan to lessen its global responsibility. However, because of infrequent knowledge, shortage of access to pharmaceutical centers, and costly schemes worldwide, most probably in emerging nations, the vulnerable subject populations cannot regularly experience the test. So, we need a clinical screening analysis to diagnose cervical cancer early and support the doctor to heal and evade cervical cancer?s spread in women?s other organs and save several lives. Thus, this paper introduces a novel hybrid approach to solve such problems: a hybrid feature selection approach with the Bayesian optimization-based optimized CatBoost (HFS-OCB) method to diagnose and predict cervical cancer risk. Genetic algorithm and mutual information approaches utilize feature selection methodology in this suggested research and form a hybrid feature selection (HFS) method to generate the most significant features from the input dataset. This paper also utilizes a novel Bayesian optimization-based hyperparameter optimization approach: optimized CatBoost (OCB) method to provide the most optimal hyperparameters for the CatBoost algorithm. The CatBoost algorithm is used to classify cervical cancer risk. There are two real-world, publicly available cervical cancer-based datasets utilized in this suggested research to evaluate and verify the suggested approach?s performance. A 20-fold cross-validation strategy and a renowned performance evaluation metric are utilized to assess the suggested approach?s performance. The outcome implies that the possibility of forming cervical cancer can be efficiently foretold using the suggested HFS-OCB method. Therefore, the suggested approach?s indicated result is compared with the other algorithms and provides the prediction. Such a predicted result shows that the suggested approach is more capable, reliable, scalable, and more effective than the other machine learning algorithms.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04825-8","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Cervical cancer is the most prevailing woman illness globally. Since cervical cancer is a very preventable illness, early diagnosis exhibits the most adaptive plan to lessen its global responsibility. However, because of infrequent knowledge, shortage of access to pharmaceutical centers, and costly schemes worldwide, most probably in emerging nations, the vulnerable subject populations cannot regularly experience the test. So, we need a clinical screening analysis to diagnose cervical cancer early and support the doctor to heal and evade cervical cancer?s spread in women?s other organs and save several lives. Thus, this paper introduces a novel hybrid approach to solve such problems: a hybrid feature selection approach with the Bayesian optimization-based optimized CatBoost (HFS-OCB) method to diagnose and predict cervical cancer risk. Genetic algorithm and mutual information approaches utilize feature selection methodology in this suggested research and form a hybrid feature selection (HFS) method to generate the most significant features from the input dataset. This paper also utilizes a novel Bayesian optimization-based hyperparameter optimization approach: optimized CatBoost (OCB) method to provide the most optimal hyperparameters for the CatBoost algorithm. The CatBoost algorithm is used to classify cervical cancer risk. There are two real-world, publicly available cervical cancer-based datasets utilized in this suggested research to evaluate and verify the suggested approach?s performance. A 20-fold cross-validation strategy and a renowned performance evaluation metric are utilized to assess the suggested approach?s performance. The outcome implies that the possibility of forming cervical cancer can be efficiently foretold using the suggested HFS-OCB method. Therefore, the suggested approach?s indicated result is compared with the other algorithms and provides the prediction. Such a predicted result shows that the suggested approach is more capable, reliable, scalable, and more effective than the other machine learning algorithms.
期刊介绍:
The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to):
Pervasive/Ubiquitous Computing and Applications
Cognitive wireless sensor network
Embedded Systems and Software
Mobile Computing and Wireless Communications
Next Generation Multimedia Systems
Security, Privacy and Trust
Service and Semantic Computing
Advanced Networking Architectures
Dependable, Reliable and Autonomic Computing
Embedded Smart Agents
Context awareness, social sensing and inference
Multi modal interaction design
Ergonomics and product prototyping
Intelligent and self-organizing transportation networks & services
Healthcare Systems
Virtual Humans & Virtual Worlds
Wearables sensors and actuators